Optimized Modelling and Evaluation of Wireless Sensor Networks for Intrusion Detection in Military Applications

Abstract

Wireless sensor networking is a promising technology with a wide range of applications, including health care and defense. The security of Wireless Sensor Networks (WSNs) is a major concern, particularly for applications where confidentiality is of the utmost importance, because, despite their attractive features (e.g., low installation cost, unattended network operation), these networks do not have a physical line of defense. So, to run WSNs securely, it's important to identify breaches before attackers damage the network or the data sink or base station, which are the nodes that collect and store information.  Enhancing the intrusion detection system was suggested in this study using a recurrent Convolutional Neural Network (RCNN) optimised using Particle Swarm Optimisation (PSO) and Coral Reef Optimisation (CRO). Successful intrusion detection has been achieved using the suggested method. When combined, PSO and CRO greatly enhance the efficacy and precision of threat identification. Everyone provides a novel and very effective method to resolve the vulnerabilities that endanger critical infrastructure and safeguard WSNs against them.